Annotation of entities and relations in Spanish radiology reports
Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | , , , , , , , , |
| Formato: | Acta de conferencia Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
Association for Computational Linguistics (ACL)
2017
|
| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 08866caa a22009017a 4500 | ||
|---|---|---|---|
| 001 | PAPER-17823 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518204912.0 | ||
| 008 | 190410s2017 xx ||||fo|||| 10| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-85045751535 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Cotik, V. | |
| 245 | 1 | 0 | |a Annotation of entities and relations in Spanish radiology reports |
| 260 | |b Association for Computational Linguistics (ACL) |c 2017 | ||
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a Chapman, W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanan, B.G., A simple algorithm for identifying negated findings and diseases in discharge summaries (2001) J. Biomed Inform., 34 (5), pp. 301-310 | ||
| 504 | |a Cohen, J., A coefficient of agreement for nominal scales (1960) Educational and Psychological Measurement, 20 (1), pp. 37-46 | ||
| 504 | |a Cotik, V., Stricker, V., Vivaldi, J., Rodriguez, H., Syntactic methods for negation detection in radiology reports in Spanish (2015) ACL - Workshop on Replicability and Reproducibility in Natural Language Processing: Adaptative Methods, Resources and Software, , Buenos Aires, Argentina | ||
| 504 | |a Cruz, N., Morante, R., López, M.J.M., Vázquez, J.M., Calderón, C.L.P., Annotating negation in Spanish clinical texts (2017) Proceedings of the Workshop Computational Semantics Beyond Events and Roles, pp. 53-58. , Association for Computational Linguistics, Valencia, Spain | ||
| 504 | |a Do, B., Wu, A.S., Maley, J., Biswal, S., Automatic retrieval of bone fracture knowledge using natural language processing (2013) J Digit Imaging, 26 (4), pp. 709-713 | ||
| 504 | |a Lakhani, P., Langlotz, C.P., Automated detection of radiology reports that document non-routine communication of critical or significant results (2009) J Digit Imaging, 23 (6), pp. 647-657 | ||
| 504 | |a Marimon, M., Vivaldi, J., Bel, N., Annotation of negation in the iula Spanish clinical record corpus (2017) Proceedings of the Workshop Computational Semantics Beyond Events and Roles, pp. 43-52. , Association for Computational Linguistics, Valencia, Spain | ||
| 504 | |a Morioka, C., Meng, F., Taira, R., Sayre, J., Zimmerman, P., Ishimitsu, D., Huang, J., El-Saden, S., Automatic classification of ultrasound screening examinations of the abdominal aorta (2016) J. Digit Imaging, 29 (6), pp. 742-748 | ||
| 504 | |a Mykowiecka, A., Marciniak, M., Kupsc, A., Rule-based information extraction from patients' clinical data (2009) Journal of Biomedical Informatics, 42 (5), pp. 923-936. , Biomedical Natural Language Processing | ||
| 504 | |a Névéol, A., Grouin, C., Tannier, X., Hamon, T., Kelly, L., Goeuriot, L., Zweigenbaum, P., CLEF ehealth evaluation lab 2015 task lb: Clinical named entity recognition (2015) Working Notes of CLEF 2015 - Conference and Labs of the Evaluation Forum, , Toulouse, France, September 8-11, 2015 | ||
| 504 | |a Oronoz, M., Gojenola, K., Perez, A., De Ilarraza, A.D., Casillas, A., On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions (2015) Journal of Biomedical Informatics, 56, pp. 318-332 | ||
| 504 | |a Pradhan, S., Elhadad, N., South, B.R., Martinez, D., Christensen, L., Vogel, A., Suominen, H., Savova, G., Evaluating the state of the art in disorder recognition and normalization of the clinical narrative (2014) Journal of the American Medical Informatics Association | ||
| 504 | |a Pradhan, S., Elhadad, N., South, B.R., Martinez, D., Christensen, L.M., Vogel, A., Suominen, H., Savova, G.K., Task 1: ShARe/CLEF eHealth evaluation lab 2013 (2013) Working Notes for CLEF 2013 Conference, , Valencia, Spain, September 23-26, 2013 | ||
| 504 | |a Roller, R., Uszkoreit, F.X.H., Seiffe, L., Mikhailov, M., Staeck, O., Budde, K., Halleck, F., Schmidt, D., A fine-grained corpus annotation schema of German nephrology records (2016) Proceedings of the Clinical Natural Language Processing Workshop, 28 (1), pp. 69-77 | ||
| 504 | |a Sevenster, M., Van Ommering, R., Qian, Y., Automatically correlating clinical findings and body locations in radiology reports using MedLEE (2012) J Digit Imaging, 25 (2), pp. 240-249 | ||
| 504 | |a Shatkay, H., John Wilbur, W., Rzhetsky, A., (2005) Annotation Guidelines, , [Online; accessed 28-04-2017] | ||
| 504 | |a Skeppstedt, M., Kvist, M., Nilsson, G.H., Dalianis, H., Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study (2014) Journal of Biomedical Informatics, 49, pp. 148-158 | ||
| 504 | |a Stenetorp, P., Pyysalo, S., Topic, G., Ohta, T., Ananiadou, S., Tsujii, J., Brat: A web-based tool for NLP-assisted text annotation (2012) Proceedings of the Demonstrations Session at EACL 2012, , Association for Computational Linguistics, Avignon, France | ||
| 504 | |a Uzuner, O., South, B.R., Shen, S., DuVall, S.L., 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text (2011) Journal of the American Medical Informatics Association, 18 (5), pp. 552-556 | ||
| 504 | |a John Wilbur, W., Rzhetsky, A., Shatkay, H., New directions in biomedical text annotation: Definitions, guidelines and corpus construction (2006) BMC Bioinformatics, 356 (7)A4 - | ||
| 520 | 3 | |a Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge. Supervised machine learning methods are very popular to address information extraction, but are usually domain and language dependent. To train new classification models, annotated data is required. Moreover, annotated data is also required as an evaluation resource of information extraction algorithms. However, one major drawback of processing clinical data is the low availability of annotated datasets. For this reason we performed a manual annotation of radiology reports written in Spanish. This paper presents the corpus, the annotation schema, the annotation guidelines and further insight of the data. © 2018 Association for Computational Linguistics (ACL). All rights reserved. |l eng | |
| 536 | |a Detalles de la financiación: Bundesministerium für Wirtschaft und Energie, 01MD16011F | ||
| 536 | |a Detalles de la financiación: This research was supported by the German Federal Ministry of Economics and Energy (BMWi) through the project MACSS (01MD16011F). | ||
| 593 | |a Departamento de Computatión, FCEyN, UBA, Argentina | ||
| 593 | |a Hospital De Pediatría, Prof. Dr. Juan P. Garrahan, Argentina | ||
| 593 | |a Language Technology Lab., DFKI, Berlin, Germany | ||
| 690 | 1 | 0 | |a ARTIFICIAL INTELLIGENCE |
| 690 | 1 | 0 | |a DATA HANDLING |
| 690 | 1 | 0 | |a DEEP LEARNING |
| 690 | 1 | 0 | |a INFORMATION RETRIEVAL |
| 690 | 1 | 0 | |a INFORMATION USE |
| 690 | 1 | 0 | |a LEARNING ALGORITHMS |
| 690 | 1 | 0 | |a NATURAL LANGUAGE PROCESSING SYSTEMS |
| 690 | 1 | 0 | |a RADIATION |
| 690 | 1 | 0 | |a RADIOLOGY |
| 690 | 1 | 0 | |a SUPERVISED LEARNING |
| 690 | 1 | 0 | |a ANNOTATED DATASETS |
| 690 | 1 | 0 | |a CLASSIFICATION MODELS |
| 690 | 1 | 0 | |a INFORMATION EXTRACTION TECHNIQUES |
| 690 | 1 | 0 | |a MANUAL ANNOTATION |
| 690 | 1 | 0 | |a MEDICAL DOCTORS |
| 690 | 1 | 0 | |a RADIOLOGY REPORTS |
| 690 | 1 | 0 | |a SPANISH RADIOLOGY |
| 690 | 1 | 0 | |a SUPERVISED MACHINE LEARNING |
| 690 | 1 | 0 | |a DATA MINING |
| 700 | 1 | |a Filippo, D. | |
| 700 | 1 | |a Roller, R. | |
| 700 | 1 | |a Uszkoreit, H. | |
| 700 | 1 | |a Xu, F. | |
| 700 | 1 | |a Mitkov R. | |
| 700 | 1 | |a Temnikova I. | |
| 700 | 1 | |a Bontcheva K. | |
| 700 | 1 | |a Nikolova I. | |
| 700 | 1 | |a Angelova G. | |
| 711 | 2 | |d 2 September 2017 through 8 September 2017 |g Código de la conferencia: 135740 | |
| 773 | 0 | |d Association for Computational Linguistics (ACL), 2017 |g v. 2017-September |h pp. 177-184 |p Int. Conf. Recent Adv. Nat. Lang. Proces., RANLP |n International Conference Recent Advances in Natural Language Processing, RANLP |x 13138502 |z 9789544520489 |t 11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017 | |
| 856 | 4 | 1 | |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045751535&doi=10.26615%2f978-954-452-049-6-025&partnerID=40&md5=2e29edf834c323a2511152b178fc8153 |y Registro en Scopus |
| 856 | 4 | 0 | |u https://doi.org/10.26615/978-954-452-049-6-025 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_13138502_v2017-September_n_p177_Cotik |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13138502_v2017-September_n_p177_Cotik |y Registro en la Biblioteca Digital |
| 961 | |a paper_13138502_v2017-September_n_p177_Cotik |b paper |c PE | ||
| 962 | |a info:eu-repo/semantics/conferenceObject |a info:ar-repo/semantics/documento de conferencia |b info:eu-repo/semantics/publishedVersion | ||
| 999 | |c 78776 | ||